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Application of Machine Learning in Animal Disease Analysis and Prediction

Author(s):

Shuwen Zhang, Qiang Su* and Qin Chen   Pages 1 - 13 ( 13 )

Abstract:


Major animal diseases pose a great threat to animal husbandry and human beings. With the deepening of globalization and the abundance of data resources, the prediction and analysis of animal diseases by using big data are becoming more and more important. The focus of machine learning is to make computers learn how to learn from data and use the learned experience to analyze and predict. Firstly, this paper introduces the animal epidemic situation and machine learning. Then it briefly introduces the application of machine learning in animal disease analysis and prediction. Machine learning is mainly divided into supervised learning and unsupervised learning. Supervised learning includes support vector machines, naive bayes, decision trees, random forests, logistic regression, artificial neural networks, deep learning, and AdaBoost. Unsupervised learning has maximum expectation algorithm, principal component analysis hierarchical clustering algorithm and maxent. Through the discussion of this paper, people have a clearer concept of machine learning and understand its application prospect in animal diseases.

Keywords:

Machine learning, animal disease, supervised learning, unsupervised learning, prediction, ensemble learning

Affiliation:

School of Life Sciences, Shanghai University, Shanghai 200444, Center for Bioinformatics and Computational Biology, Pai Chai University, Daejeon, School of Life Sciences, Shanghai University, Shanghai 200444



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